Traditional MR CS reconstruction techniques are iterative algorithms that usually require a sparsifying transform that when combined with regularization parameters are able to find a solution for these ill-posed inverse problems [1]}, [2]}. The drawback of these iterative approaches, however, is that they are time-consuming, making them more difficult to incorporate in a near real-time MR imaging scenario (i.e., where images are reconstructed and displayed on the scanner during the procedure).
